Various types of algorithms for identifying horizons within a geological volume of interest are known. Typically, these algorithms tend either to focus on following large-scale features of data representing the geological volume of interest, or to focus on following more local features of the data. For example, stratal-slicing solutions that use one or more reference horizons to interpolate and/or extrapolate a whole series of horizons through the geological volume of interest tend to be more faithful to large-scale features. These solutions tend to miss, or be inaccurate with respect to, local features of the underlying data.
It is also known that individual horizons can be moved, adjusted, or “snapped” to align with an individual feature present in the data representing a geological volume of interest. For example, an individual horizon can be snapped to a local trough or peak. However, these techniques often cause jumps or discontinuities because points along a single horizon may snap to different events. Therefore, this typically requires manual oversight, if not direct control, and only aligns an individual horizon with an individual feature.